Advertisement

Impact of Fingerprint Image Quality on Matching Score

  • P. Thejaswini
  • R. S. Srikantaswamy
  • A. S. Manjunatha
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)

Abstract

Biometric Fingerprint image vary due to various environmental conditions like temperature, humidity, weather etc. Such variations are considered as noise introduced into the Fingerprint image. The variations of Fingerprint image leads to varied matching score produced from the matching algorithm in case of authentication system. The variation of matching score leads to poor recognition of Fingerprints, which affects the recognition of user in an authentication system. A study is conducted to understand the matching score variations due to different levels of noise present in the Fingerprint image. This study leads to understand the noise levels, which affects the matching score of Fingerprint image and also leads to find the threshold noise level beyond which the matching of Fingerprint image fails.

Keywords

Biometric image Matching score Threshold noise Access control Authentication Minutiae Fingerprint noise Gaussian noise Variance MSR PSNR SSIM Adaptive biometric 

References

  1. 1.
    Saini K, Dewal M (2010) Designing of a virtual system with fingerprint security by considering many security threats. Int. J. Comput. Appl. IJCA 3:25–31Google Scholar
  2. 2.
    Jain AK, Feng J, Nandakumar K (2010) Fingerprint matching. Comput 43(2):36–44Google Scholar
  3. 3.
    Theofanos M, et al (2006) Does habituation affect fingerprint quality?. CHI’06 extended abstracts on human factors in computing systems. ACMGoogle Scholar
  4. 4.
    Thejaswini P, Srikantaswamy R, Manjunatha AS (2015) Environmental impact on biometric traits and methods to improve biometric recognition system, doi:  10.3850/978-981-09-6200-5_32
  5. 5.
    Prasad R. Satya, Al-Ani MS, Nejres SM (2015) An efficient approach for fingerprint recognition. Image 4(5)Google Scholar
  6. 6.
    Bana S, Kaur D (2011) Fingerprint recognition using image segmentation. Int J Adv Eng Sci Technol 5:1Google Scholar
  7. 7.
    Uludag U, Ross A, Jain AK (2004) Biometric template selection and update: a case study in fingerprints. Pattern Recogn 37(7):1533–1542Google Scholar
  8. 8.
    Verma R, Ali J (2013) A comparative study of various types of image noise and efficient noise removal techniques. Int J Adv Res Comput Sci Softw Eng 3(10):617–622Google Scholar
  9. 9.
    Xie SJ, Yoon S, Shin JW, Park DS (2010b) Effective fingerprint quality estimation for diverse capture sensors. Sensors 10(9):7896–7912Google Scholar
  10. 10.
    Mythili C, Kavitha V (2011) Efficient technique for color image noise reduction. Res Bull Jordan, ACM 1(11):41–44Google Scholar
  11. 11.
    Mythili C, Kavitha V (2011) Efficient technique for color image noise reduction. Res Bull Jordan ACM 2(III)Google Scholar
  12. 12.
    Patidar P, et al. Image De-noising by various filters for different noise. Int J Comput Appl 9(4):0975–8887Google Scholar
  13. 13.
    Boyat A, Joshi BK (2013) Image denoising using wavelet transform and median filtering. In: IEEE Nirma University International Conference on Engineering, AhmadabadGoogle Scholar
  14. 14.
    Barbu T (2013) Variational image denoising approach with diffusion porous media flow. Abstr Appl Anal 2013(856876):8 (Hindawi Publishing Corporation)Google Scholar
  15. 15.
    Arpana MA, Kiran P (2014) Feature extraction values for digital mammograms. Int J Soft Comput Eng (IJSCE) 4(2):183–187Google Scholar
  16. 16.
    Thakur N, Devi S (2011) A new method for color image quality assessment. Int J Comput Appl 15(2):1–10Google Scholar
  17. 17.
    Girod B (1993) What’s wrong with mean-squared error. Digital Images Hum Vis 207–220 (Watson AB, ed),Google Scholar
  18. 18.
    Wang Z, Bovik AC (2009) Mean squared error: love it or leave it?—a new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98–117Google Scholar
  19. 19.
    Singh V (2009) Design of a neuro fuzzy model for image compression in wavelet domain. J Ind Soc of Remote Sens 37(2):185–199Google Scholar
  20. 20.
    An introduction to image compression. http://www.Debugmode.com/imagecmp/
  21. 21.
    Nisha SK, Kumar S (2013) Image quality assessment techniques. Int J Adv Res Comput Sci Softw Eng 3(7):636–640Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • P. Thejaswini
    • 1
  • R. S. Srikantaswamy
    • 2
  • A. S. Manjunatha
    • 3
  1. 1.Department of ECEJSSATEBengaluruIndia
  2. 2.Department of ECESITTumkurIndia
  3. 3.Department of CSESITTumkurIndia

Personalised recommendations